Swarm Learning in Digital Pathology: Revolutionizing Cancer Histopathology

Today on the Digital Pathology Podcast my guest is Oliver Saldana, the first author of a significant Nature Medicine paper published in 2022 on 'Swarm Learning for Decentralized Artificial Intelligence in Cancer Histopathology'.

Oliver shares his journey from Mangalore, India, to Germany, where he pursued his master's and PhD, delving into histopathology and decentralized AI under the supervision of Professor Dr. Jakob Nicolas Kather.

The discussion explores the concept of swarm learning as a novel method for deep learning in histopathology, its advantages over centralized learning including compliance with data protection laws like GDPR, and its potential for global collaboration in medical research without sharing sensitive data.

Oliver emphasizes swarm learning’s ease of setup and its alignment with the FAIR principles for scientific data management. The podcast aims to shed light on the groundbreaking work being done in the convergence of pathology and computer science, urging researchers and pathology centers to digitize their slides and contribute to global swarm learning projects.

00:00 Introduction to Swarm Learning and Its Applications
00:50 Intro
01:17 Meet Oliver Saldana: A Trailblazer in Decentralized AI for Cancer Histopathology
03:57 Exploring the Concept of Decentralized AI and Its Importance
06:52 Understanding Centralized vs. Decentralized Learning
08:47 The Revolutionary Approach of Swarm Learning
10:38 Blockchain's Role in Enhancing Histopathology with Swarm Learning
14:50 Addressing Preprocessing and Generalizability in Swarm Learning
21:26 Swarm Learning's Compliance with GDPR and Data Protection
25:05 Exploring Swarm Learning in Medical Data Analysis
25:34 Prototype Study and Real Cohorts in Swarm Learning
27:01 Comparing Swarm Learning with Centralized Models
27:44 The Role of Bare Metal Servers in Swarm Learning
30:01 Centralized Slide Repositories vs. Swarm Learning
44:11 Commercializing Swarm Learning Models
47:07 FAIR Principles and Swarm Learning
51:11 Global Ambitions and the Future of Swarm Learning

THIS EPISODES RESOURCES

📝 Swarm Learning for decentralized and confidential clinical machine learning
🔗 https://www.nature.com/articles/s41586-021-03583-3

📝The FAIR Guiding Principles for scientific data management and stewardship
🔗https://www.nature.com/articles/sdata201618

🎧BIGPICTURE – THE LARGEST WHOLE SLIDE REPOSITORY FOR AI MODEL DEVELOPMENT IN PATHOLOGY. WHERE DO WE STAND AT MONTH 15/72?
🔗https://digitalpathologyplace.com/podcast/bigpicture-the-largest-whole-slide-repository-for-ai-model-development-in-pathology-where-do-we-stand-at-month-15-72/

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Navigating Ethical Challenges in AI-Powered Pathology

This episode is a webinar recording.
It delves into the complex ethical considerations of incorporating artificial intelligence (AI) in pathology.

Dr. Zuraw begins by exploring the fundamentals of ethics and moves on to discuss the impact of AI in pathology, focusing on:

• ethical dilemmas,
• data diversity issues,
• biases, and
• the importance of maintaining professional and societal ethical standards in the wake of AI integration.

The session touches upon the ethical guidelines and regulatory frameworks guiding ethical decision-making in healthcare, alongside the role of regulatory agencies like the FDA.

It also highlights the significance of data diversity and mitigation strategies to address potential ethical pitfalls in AI utilization.

The webinar emphasizes the constant balance between advancing technology and ethical responsibility, underlining the need for transparency, governance, and accountability in deploying AI tools in pathology.

00:00 Introduction to Ethics in AI-Powered Pathology
00:30 Exploring the Ethical Dilemmas in Healthcare
00:36 Webinar Overview and Digital Pathology Insights
01:05 Defining Ethics and Its Importance in AI Pathology
02:05 Interactive Webinar Engagement and Audience Participation
03:25 Deep Dive into Ethics: Definitions and Applications
09:31 Ethical Considerations in Biomedical Research
10:34 Navigating Ethical Dilemmas: A Practical Example
13:57 Understanding Ethical Principles in Decision Making
17:14 AI Bias and Representation in Pathology
20:52 Frameworks and Guidelines for Ethical Oversight
25:02 AI Applications in Pathology: Ethical Perspectives
27:21 Exploring AI in Research and Its Capabilities
29:22 AI's Role in Medical Imaging and Diagnostics
33:11 Ethical Considerations and AI in Pathology
34:04 Addressing AI Challenges: Bias, Interpretability, and Security
44:26 AI as a Medical Device: Regulatory Perspectives and Future Directions
49:16 Concluding Thoughts and Audience Engagement


THIS EPISODES RESOURCES:

• Interested in getting the slides from this presentation, click here
• AI in Pathology: What could possibly go wrong (Paper)
• Ethics of AI in Pathology: Current Paradigms and Emerging Issues (Paper)
• Guardrails for the use of generalist AI in cancer care (Nature commentary)
• No more microscopes. How close are we to glassless pathology? W/ Dr. Richard Levenson, US Davis Health (Podcast episode)

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If any of the statements applies:

➡️ You know AI and Machine Learning are already part of the pathology workflow, but maybe you are not exactly sure which part of the workflow?

➡️ “AI” is still a bit of overhyped, fuzzy buzzword for you?

➡️ You would like to learn about how it can help pathologist and labs work smarter and patients get better care.

Then this webinar is for you!

This is the second part of the “Digital Pathology 101” webinar series, based on the “Digital Pathology 101” book, where Dr. Aleks Zuraw explains digital pathology and AI concepts.

This journey through Chapter 3 illuminates how image analysis, AI, and machine learning not only complement traditional pathology but propel it into new realms of precision and insight.

As we delve into the essence of tissue image analysis and the transformative role of AI and machine learning in modern pathology, you'll discover how these technologies augment diagnostic methods, enhance research, and redefine what's possible in our field.

From the basics of tissue image analysis to the advanced realms of computer vision and the pivotal role of quality control, this webinar bridges the gap between high-level computational domains and daily pathology practice.

What You'll Explore:

The foundational principles of image analysis, AI, and machine learning in pathology.
The crucial balance between classical and AI-based approaches to tissue image analysis and their applications in both regulated and non-regulated environments.
The importance of quality control in ensuring accurate, reliable results from AI-assisted analyses.
An introduction to the key terminology of pathology informatics, demystifying the language that underpins digital pathology and AI.
Who Should Attend:
This webinar is tailored for:

🔴 pathologists,

🔴 researchers, and

🔴 healthcare professionals

who are eager to learn about and/or integrate AI and machine learning into their work.

Whether you're just starting or looking to deepen your expertise in digital pathology, this series offers invaluable insights into leveraging technology for enhanced diagnostic precision and patient care.

Date and Time:
April 11, 2024 at 9:00 - 10:30 a.m., EST

Location:
Online

AI is here, so let’s learn what it means for pathology

how can you leverage it for your work?

and how to navigate this new technology responsibly.


Looking forward to seeing you on the inside!

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Become a Digital Pathology Trailblazer and See you inside the club: Digital Pathology Club Membership